SARS-CoV-2 is the RNA virus responsible for COVID-19, the prognosis of which has been found to be slightly worse in men. The present study aimed to analyze the expression of different mRNAs and their regulatory molecules (miRNAs and lncRNAs) to consider the potential existence of sex-specific expression patterns and COVID-19 susceptibility using bioinformatics analysis. The binding sites of all human mature miRNA sequences on the SARS-CoV-2 genome nucleotide sequence were predicted by the miRanda tool. Sequencing data was excavated using the Galaxy web server from GSE157103, and the output of feature counts was analyzed using DEseq2 packages to obtain differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and DEG annotation analyses were performed using the ToppGene and Metascape tools. Using the RNA Interactome Database, we predicted interactions between differentially expressed lncRNAs and differentially expressed mRNAs. Finally, their networks were constructed with top miRNAs. We identified 11 miRNAs with three to five binding sites on the SARS-COVID-2 genome reference. MiR-29c-3p, miR-21-3p, and miR-6838-5p occupied four binding sites, and miR-29a-3p had five binding sites on the SARS-CoV-2 genome. Moreover, miR-29a-3p, and miR-29c-3p were the top miRNAs targeting DEGs. The expression levels of miRNAs (125, 181b, 130a, 29a, b, c, 212, 181a, 133a) changed in males with COVID-19, in whom they regulated ACE2 expression and affected the immune response by affecting phagosomes, complement activation, and cell-matrix adhesion. Our results indicated that XIST lncRNA was up-regulated, and TTTY14, TTTY10, and ZFY-AS1 lncRN as were down-regulated in both ICU and non-ICU men with COVID-19. Dysregulation of noncoding-RNAs has critical effects on the pathophysiology of men with COVID-19, which is why they may be used as biomarkers and therapeutic agents. Overall, our results indicated that the miR-29 family target regulation patterns and might become promising biomarkers for severity and survival outcome in men with COVID-19.

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

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